To address these questions, we examine three popular image datasets, CIFAR-10 [1]}, SVHN [2]}, and CIFAR-100 [1]}. CIFAR-10 and SVHN each contain ten classes. The training sets are exponentially imbalanced (maximum imbalance ratio of 100:1), similar to Cui et al. [4]}. CIFAR-100, which contains 100 classes and has 10 times fewer training examples per class than CIFAR-10 and SVHN, was exponentially imbalanced with a ratio of 10:1 to allow for sufficient training examples in the minority classes.
